The frequency spectrum of a signal is typically degraded as it passes through a transmission medium, such as a cable. This is a concern, for example, in the implementation of local area networks (LANs), which may require signals having large bandwidths to be transmitted over various distances. This is particularly true in the 100BASE-TX Fast Ethernet LAN protocol, which requires at least 70 MHZ of bandwidth for undistorted transmission through the network at the desired data rate of 100 Mb/s. This degradation usually takes the form of attenuation of the high-frequency components of the signal's frequency spectrum. As a result of this degradation, narrow signal pulses have lower peak amplitudes than wide pulses, causing difficulty in recovering the bit information encoded in each pulse.
To compensate for frequency degradation, a processing technique called cable equalization is often performed, which restores the attenuated frequency components almost completely back to their former amplitudes. FIG. 1 illustrates this concept. An ideal signal (s(t)) has its frequency spectrum degraded (i.e., its high-frequency components are attenuated) as a result of passing through a transmission medium. The degraded signal (s.sub.d (t)) is then restored through equalization to produce a restored signal (s.sub.e (t)). Once the signal has been restored, it may then be processed by other downstream components, for example, a clock and data recovery circuit, in a conventional manner.
A further result of frequency spectrum degradation is signal "jitter," i.e., signal transitions do not occur at multiples of a fixed time interval but, rather, at multiples of a varying time interval. Jitter reduces the ability to recover data from the signal. One way of observing this reduction in the ability to recover data is to observe the signal "eye pattern." A signal eye pattern is obtained by using an oscilloscope to observe the signal while triggering the oscilloscope trace with the signal itself.
FIG. 2 illustrates the eye patterns that would be obtained for the three waveforms of FIG. 1 (s(t), s.sub.d (t) and s.sub.e (t)). For the best sampling of a signal, the sampling transition should be located in the center of the eye, which provides maximum setup and hold times for signal sampling. As can be seen from FIG. 2, the eye of the signal exiting the transmission medium (s.sub.d (t)) is practically closed. Thus, recovering data from this signal would be practically impossible since little or no setup and hold times are available for reliable sampling by the sampling clock. On the other hand, the signal eye for the signal resulting from equalization (s.sub.e (t)) is practically completely open, thereby restoring the large setup and hold times needed for reliable sampling by the sampling clock.
Often the characteristics of the transmission medium are allowed to vary significantly while at the same time requiring good equalization. For example, if the transmission medium is a cable, the cable may be allowed to range in length from zero to 100 m, as is the case in 100BASE-TX Ethernet networks. Since short cable lengths tend to degrade a signal's frequency spectrum much less than long cable lengths, an equalizer designed for a short cable will generally under-compensate a long cable. Conversely, an equalizer designed for a long cable will over-compensate a short cable. In either case, the resulting signal may be unintelligible.
An "adaptive" equalizer solves this problem by automatically varying its characteristics ("adapting" its characteristics) as a function of the transmission medium characteristics. Thus, an adaptive equalizer produces an output signal that is optimized for any transmission medium that is within specified limits. FIG. 3 illustrates the block diagram of a prior art adaptive equalizer implementation. An input signal i(t) carried by a transmission medium is provided to a variable filter 302 whose characteristics are varied under feedback control. An output signal o(t) of variable filter 302 is input to a detector 304 which converts this signal into a restored waveform r(t).
The output signal o(t) of variable filter 302 is also provided to a summing element 306 in inverted form, and the restored signal r(t) is likewise provided to summing element 306. Thus, the output of variable filter 302 is subtracted from the output of detector 304 to generate an error signal e(t). This error signal e(t) represents the distortion in the restored signal r(t) caused by imperfect compensation by variable filter 302 of the input signal i(t) from the transmission medium. The error signal e(t) is provided to variable filter 302 such that the error signal e(t) modifies the characteristics of variable filter 302 in a direction that reduces the error.
This prior art implementation of an adaptive equalizer has several drawbacks. For example, the prior art adaptive equalizer requires that the amplitude and timing of the variable filter output be precisely controlled so that the error signal represents only true signal distortion. Without precise amplitude and delay control, the error signal would include false contributions from amplitude and timing differences between the variable filter output and the detector output. So this implementation requires using very precise analog techniques for operation.
Furthermore, the prior art adaptive equalizer implementation uses an amplitude criterion for adapting the filter, that is, it examines amplitude differences and generates an error signal based on these differences. However, the criterion that is of direct interest to the equalization process is not amplitude, but rather jitter, since jitter directly impacts the ability to accurately perform sampling. While, in theory, equalizing a signal's amplitude over all frequencies of interest is expected to result in minimum jitter, this might not necessarily be true for practical--and, therefore, imperfect--filter implementations.